Azure OpenAI BYOD (Bring Your Own Data) empowers companies to utilize AI models like GPT-3.5, GPT-4, DALL-E, and Whisper for various applications such as conversation, content generation, text-to-image synthesis, and speech-to-text transcription. Setting up involves accessing Azure resources, uploading data, and implementing models with estimated monthly costs based on usage. Including SharePoint data enhances AI capabilities but raises security risks like data theft.

Introduction

In today’s business world, staying ahead means using the best technologies to work better, faster, and smarter. Azure OpenAI BYOD lets companies incorporate advanced AI in their workflow with their own data. Let’s have a look at how this service can used and the considerations involved in it.

One technology, different methodologies

First of all, let’s have a look at how corporate companies can benefit from this advanced service to improve productivity and deliver better experiences to customers and employees alike:

• Customer support chatbots: You can use AI chatbots powered by Azure OpenAI to handle customer inquiries and support requests. These bots can understand questions and provide helpful responses instantly, improving customer satisfaction and reducing the workload on human support agents.
• Text-to-Image: It can generate visuals from textual descriptions, aiding in product design, marketing material creation, and data visualization.
• Speech-to-Text: Transcribe meetings, interviews, and customer calls accurately, facilitating documentation, analysis, and sharing details with employees.
• Automated Report Generation: You can automate the generation of reports, such as sales reports or financial summaries. The AI can analyse data from various sources, summarize key insights, and generate detailed reports in a fraction of the time it would take manually.
• Content Creation Assistance: Marketing teams can utilize Azure OpenAI to assist in content creation tasks, such as writing blog posts, social media updates, or marketing copy. The AI can suggest ideas, draft content, and even provide grammar and style suggestions, saving time and ensuring consistency in productivity.
• Employee assistance: By incorporating Azure OpenAI, your company can expand its internal knowledge base quickly and efficiently. Employees can ask questions, and the AI can provide relevant answers based on existing documents, policies, and procedures, facilitating knowledge sharing and problem-solving within the organization.
• Data Analysis and Insights: Azure OpenAI can help analyse large datasets and extract valuable insights for decision-making. For example, the AI can identify trends, predict customer behaviour, or highlight areas for process optimization, enabling the company to make data-driven decisions more effectively.
• Training and Onboarding: HR departments can use it to develop interactive training materials and onboarding programs for new employees. The AI can create simulated scenarios, quizzes, and interactive modules to enhance learning experiences and ensure consistent training delivery across the organization.
• Multilingual Communication: For companies operating globally, this feature can assist in multilingual communication. The AI can translate documents, emails, or customer inquiries in real time, enabling seamless communication across language barriers and enhancing international collaboration.

OpenAI models available today

Let’s have a look at some existing OpenAI models from which you can have an idea of what these models would look like once implemented.

• GPT-3.5: This model, known as the Generative Pre-trained Transformer 3.5, is a natural language processing model. It excels in tasks such as conversational AI, search, summarization, writing assistance, content generation, and code-based scenarios. GPT-3.5 is capable of understanding and generating human-like text based on the input provided, making it the best choice for various language-related applications.
• GPT-3.5 Turbo: GPT-3.5 Turbo refers to an enhanced version of the GPT-3.5 model that offers improved performance and efficiency. It adds to the capabilities of GPT-3.5, providing faster response times, higher accuracy, and enhanced language understanding.
• GPT-4: GPT-4 is an update in the Generative Pre-trained Transformer series, offering advancements in natural language understanding and generation
• GPT-4 Turbo: Similar to GPT-3.5 Turbo, GPT-4 Turbo implies an enhanced version of the GPT-4 model with additional optimizations and improvements. It combines advanced language processing with other practices such as vision, enabling more comprehensive and detail-oriented.
• Embedding Models: Embedding models are special tools that take information and turn it into simple codes, kind of like secret messages. These codes help computers understand words, phrases, or documents better by capturing how similar or related they are. These models are important for AI because they help with things like talking to computers, finding things online, suggesting stuff you might like, and understanding if something is positive or negative. They make it easier for computers to learn from all sorts of information, like organized or messy data, which helps them make decisions and figure things out.
• DALL-E 2 and DALL-E 3: DALL-E models specialize in transforming textual descriptions into corresponding images. DALL-E 2 and DALL-E 3 are advanced versions capable of generating high-quality, realistic images based on detailed textual prompts. These models find applications in fields such as design, art, visual storytelling, and creative content generation.
• OpenAI Whisper Model: The Whisper model is designed specifically for speech-to-text transcription. It utilizes advanced techniques in speech recognition and natural language processing to accurately convert spoken language into text. Whisper enables applications such as voice-controlled interfaces, automated transcriptions, and voice search.
• GPT-4 Turbo with Vision: GPT-4 Turbo with Vision combines the capabilities of advanced language understanding with visual comprehension. This model can analyse and generate text based on both textual and visual inputs, enabling more detailed and contextually relevant interactions. It has applications in areas such as content creation, image captioning, visual question answering, and multimodal reasoning.

Step-by-step procedure

Here’s how you can incorporate Azure OpenAI models using your own data

• Access Request form: Fill out the access request form provided by Microsoft. Provide necessary details about your company and intended use of Azure OpenAI BYOD.
• Define Requirements: Clearly outline your requirements, including which OpenAI models you want to use and how you plan to integrate them with your data and applications.
• Azure Account Setup: Sign up for an Azure account if you don’t already have one. This will give you access to Azure services.
• Azure Resource Configuration: Configure the necessary Azure resources for running OpenAI models. This may include setting up virtual machines, storage accounts, and networking resources; we’ll discuss these later in the blog.
• Install Azure OpenAI BYOD SDK: Install the Azure OpenAI BYOD software development kit (SDK) on your development environment. This SDK provides tools and libraries for integrating OpenAI models with your applications.
• Integrate with Your Data: Connect Azure OpenAI BYOD to your company’s data sources, such as databases or file storage. This will allow the AI models to access and analyse your data.
• Deploy OpenAI Models: Deploy the desired OpenAI models to your Azure environment. This may involve downloading pre-trained models or training custom models using your data.
• Testing and Validation: Test the integration and functionality of the OpenAI models within your applications. Confirm that they are performing as expected and meeting your requirements.

Estimated Monthly Costs:

The monthly costs of running basic OpenAI models on Azure can vary depending on factors such as model complexity, data volume, and resource usage. However, here’s a rough estimate based on typical usage:

• Compute Resources: A basic virtual machine instance suitable for running OpenAI models might cost around $100 to $500 per month, depending on the size and specifications of the instance.
• Storage: Storing data in Azure storage accounts could cost anywhere from $10 to $1000 or more per month, depending on the amount of data stored and the type of storage used.
• Data Transfer: Data transfer costs are typically minimal, often ranging from a few cents to a few dollars per month, depending on the volume of data transferred.
• OpenAI Model Usage: Usage fees for pre-trained OpenAI models can vary based on factors such as the number of requests or API calls made to the models. These fees could range from a few cents to a few dollars per thousand requests.

You can get a more appropriate estimate using Azure’s services pricing calculator.

Incorporating SharePoint data

Incorporating SharePoint data in Azure OpenAI BYOD means combining information stored in SharePoint, with Azure’s OpenAI Bring Your Own Data service. This integration allows organizations to utilize their SharePoint data within Azure’s AI, enabling advanced analysis, processing, and insights generation.

Here’s how you can do this in simple steps:

• Access SharePoint Data: First, you need to access the data stored in your company’s SharePoint environment. This data could include documents, spreadsheets, presentations, and other files relevant to your business operations.
• SharePoint API: Azure OpenAI BYOD allows you to connect to external data sources like SharePoint using APIs (Application Programming Interfaces). You’ll need to use the SharePoint API to retrieve data from your SharePoint site programmatically.
• Authentication: Ensure that you have proper authentication credentials (such as username and password or API keys) to access the SharePoint API. This will allow Azure OpenAI BYOD to securely connect to your SharePoint data without compromising security.
• Data Extraction: Use the SharePoint API to extract the specific data you need for your AI models. This could involve querying for documents based on certain criteria, extracting text from documents, or retrieving data associated with files.
• Data Transformation (Optional): Depending on the format of the data retrieved from SharePoint, you may need to transform it into a format suitable for input into your AI models. For example, you might need to convert document text into a structured format or pre-process it for natural language processing tasks.
• Integration with Azure OpenAI BYOD: Once you have extracted and prepared the SharePoint data, integrate it with Azure OpenAI BYOD. This typically involves feeding the data into your AI models for analysis, processing, or generating insights.
• Testing and Validation: Test the integration to ensure that SharePoint data is being properly used by Azure OpenAI BYOD and that the AI models are producing the desired outputs and then confirm the accuracy and reliability of the results generated.
• Monitoring and Maintenance: You may need to make any necessary adjustments or updates to the integration as your SharePoint data or AI requirements evolve over time.

Security risks associated with Azure OpenAI

As the saying goes, every cloud has a silver lining, the Azure OpenAI also comes with some risks that might put you or your company under threat if not considered. Therefore, it’s necessary to know about these details to avoid any mishaps in the future.

One significant risk is unauthorized access, where individuals might enter the Azure OpenAI BYOD environment without proper authorization. This unauthorized access could lead to the viewing, modification, or theft of sensitive data, including confidential business information or customer data

Then, there are insider threats, which present a significant risk, as employees or individuals with legitimate access to Azure OpenAI BYOD may misuse their privileges. This could involve intentional or accidental access to sensitive data, posing a considerable security risk. During data transmission between the company’s systems and Azure OpenAI BYOD, there is also a risk of data interception by unauthorized third parties. This interception occurs if data is not adequately encrypted or if secure communication protocols are not employed.

Misconfiguration of security settings or access controls within Azure OpenAI BYOD is another risk factor. Improperly configured settings can leave data vulnerable to exploitation. Furthermore, social engineering attacks, such as phishing emails or impersonation attempts, pose a threat to data security within Azure OpenAI BYOD.

Conclusion

In conclusion, Azure OpenAI BYOD offers companies a powerful toolkit to leverage OpenAI for various applications. Understanding the setup process, costs, and security considerations is crucial for maximizing benefits while safeguarding sensitive data. With proper implementation and vigilance, Azure OpenAI BYOD can help companies become for innovative and efficient.